Reactive Power Optimization of Active Distribution Network Based on Improved Multi-objective Grey Wolf Algorithm

In this paper, we propose an improved multi-objective grey Wolf algorithm to address the convergence and diversity issues in optimizing reactive power in active distribution networks. Our approach is tailored to meet the specific application requirements of this problem. This paper introduces improvement strategies for a multi-objective reactive power optimization model based on the multi-objective grey Wolf algorithm. These strategies include random opposition-learning, population update using differential evolution, and adaptive adjustment of leader selection pressure. The model aims to minimize active power network loss, voltage deviation, and the cost of discrete voltage regulator equipment action. The simulation results, based on the IEEE33 case, demonstrate that the improved algorithm exhibits superior convergence and diversity compared to the multi-objective grey Wolf algorithm. Additionally, the optimization scheme obtained through the improved algorithm results in lower active power loss, reduced voltage deviation, and decreased equipment operation cost.